---
title: Together.ai
description: Learn how to use Together.ai's models with the AI SDK.
---

# Together.ai Provider

The [Together.ai](https://together.ai) provider contains support for 200+ open-source models through the [Together.ai API](https://docs.together.ai/reference).

## Setup

The Together.ai provider is available via the `@ai-sdk/togetherai` module. You can
install it with

<Tabs items={['pnpm', 'npm', 'yarn', 'bun']}>
  <Tab>
    <Snippet text="pnpm add @ai-sdk/togetherai" dark />
  </Tab>
  <Tab>
    <Snippet text="npm install @ai-sdk/togetherai" dark />
  </Tab>
  <Tab>
    <Snippet text="yarn add @ai-sdk/togetherai" dark />
  </Tab>

  <Tab>
    <Snippet text="bun add @ai-sdk/togetherai" dark />
  </Tab>
</Tabs>

## Provider Instance

You can import the default provider instance `togetherai` from `@ai-sdk/togetherai`:

```ts
import { togetherai } from '@ai-sdk/togetherai';
```

If you need a customized setup, you can import `createTogetherAI` from `@ai-sdk/togetherai`
and create a provider instance with your settings:

```ts
import { createTogetherAI } from '@ai-sdk/togetherai';

const togetherai = createTogetherAI({
  apiKey: process.env.TOGETHER_AI_API_KEY ?? '',
});
```

You can use the following optional settings to customize the Together.ai provider instance:

- **baseURL** _string_

  Use a different URL prefix for API calls, e.g. to use proxy servers.
  The default prefix is `https://api.together.xyz/v1`.

- **apiKey** _string_

  API key that is being sent using the `Authorization` header. It defaults to
  the `TOGETHER_AI_API_KEY` environment variable.

- **headers** _Record&lt;string,string&gt;_

  Custom headers to include in the requests.

- **fetch** _(input: RequestInfo, init?: RequestInit) => Promise&lt;Response&gt;_

  Custom [fetch](https://developer.mozilla.org/en-US/docs/Web/API/fetch) implementation.
  Defaults to the global `fetch` function.
  You can use it as a middleware to intercept requests,
  or to provide a custom fetch implementation for e.g. testing.

## Language Models

You can create [Together.ai models](https://docs.together.ai/docs/serverless-models) using a provider instance. The first argument is the model id, e.g. `google/gemma-2-9b-it`.

```ts
const model = togetherai('google/gemma-2-9b-it');
```

### Reasoning Models

Together.ai exposes the thinking of `deepseek-ai/DeepSeek-R1` in the generated text using the `<think>` tag.
You can use the `extractReasoningMiddleware` to extract this reasoning and expose it as a `reasoning` property on the result:

```ts
import { togetherai } from '@ai-sdk/togetherai';
import { wrapLanguageModel, extractReasoningMiddleware } from 'ai';

const enhancedModel = wrapLanguageModel({
  model: togetherai('deepseek-ai/DeepSeek-R1'),
  middleware: extractReasoningMiddleware({ tagName: 'think' }),
});
```

You can then use that enhanced model in functions like `generateText` and `streamText`.

### Example

You can use Together.ai language models to generate text with the `generateText` function:

```ts
import { togetherai } from '@ai-sdk/togetherai';
import { generateText } from 'ai';

const { text } = await generateText({
  model: togetherai('meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo'),
  prompt: 'Write a vegetarian lasagna recipe for 4 people.',
});
```

Together.ai language models can also be used in the `streamText` function
(see [AI SDK Core](/docs/ai-sdk-core)).

The Together.ai provider also supports [completion models](https://docs.together.ai/docs/serverless-models#language-models) via (following the above example code) `togetherai.completion()` and [embedding models](https://docs.together.ai/docs/serverless-models#embedding-models) via `togetherai.embedding()`.

## Model Capabilities

| Model                                          | Image Input         | Object Generation   | Tool Usage          | Tool Streaming      |
| ---------------------------------------------- | ------------------- | ------------------- | ------------------- | ------------------- |
| `meta-llama/Meta-Llama-3.3-70B-Instruct-Turbo` | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> |
| `meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo`  | <Cross size={18} /> | <Cross size={18} /> | <Check size={18} /> | <Check size={18} /> |
| `mistralai/Mixtral-8x22B-Instruct-v0.1`        | <Cross size={18} /> | <Check size={18} /> | <Check size={18} /> | <Check size={18} /> |
| `mistralai/Mistral-7B-Instruct-v0.3`           | <Cross size={18} /> | <Check size={18} /> | <Check size={18} /> | <Check size={18} /> |
| `deepseek-ai/DeepSeek-V3`                      | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> |
| `google/gemma-2b-it`                           | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> |
| `Qwen/Qwen2.5-72B-Instruct-Turbo`              | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> |
| `databricks/dbrx-instruct`                     | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> |

<Note>
  The table above lists popular models. Please see the [Together.ai
  docs](https://docs.together.ai/docs/serverless-models) for a full list of
  available models. You can also pass any available provider model ID as a
  string if needed.
</Note>

## Image Models

You can create Together.ai image models using the `.image()` factory method.
For more on image generation with the AI SDK see [generateImage()](/docs/reference/ai-sdk-core/generate-image).

```ts
import { togetherai } from '@ai-sdk/togetherai';
import { experimental_generateImage as generateImage } from 'ai';

const { images } = await generateImage({
  model: togetherai.image('black-forest-labs/FLUX.1-dev'),
  prompt: 'A delighted resplendent quetzal mid flight amidst raindrops',
});
```

You can pass optional provider-specific request parameters using the `providerOptions` argument.

```ts
import { togetherai } from '@ai-sdk/togetherai';
import { experimental_generateImage as generateImage } from 'ai';

const { images } = await generateImage({
  model: togetherai.image('black-forest-labs/FLUX.1-dev'),
  prompt: 'A delighted resplendent quetzal mid flight amidst raindrops',
  size: '512x512',
  // Optional additional provider-specific request parameters
  providerOptions: {
    togetherai: {
      steps: 40,
    },
  },
});
```

For a complete list of available provider-specific options, see the [Together.ai Image Generation API Reference](https://docs.together.ai/reference/post_images-generations).

### Model Capabilities

Together.ai image models support various image dimensions that vary by model. Common sizes include 512x512, 768x768, and 1024x1024, with some models supporting up to 1792x1792. The default size is 1024x1024.

| Available Models                           |
| ------------------------------------------ |
| `stabilityai/stable-diffusion-xl-base-1.0` |
| `black-forest-labs/FLUX.1-dev`             |
| `black-forest-labs/FLUX.1-dev-lora`        |
| `black-forest-labs/FLUX.1-schnell`         |
| `black-forest-labs/FLUX.1-canny`           |
| `black-forest-labs/FLUX.1-depth`           |
| `black-forest-labs/FLUX.1-redux`           |
| `black-forest-labs/FLUX.1.1-pro`           |
| `black-forest-labs/FLUX.1-pro`             |
| `black-forest-labs/FLUX.1-schnell-Free`    |

<Note>
  Please see the [Together.ai models
  page](https://docs.together.ai/docs/serverless-models#image-models) for a full
  list of available image models and their capabilities.
</Note>

## Embedding Models

You can create Together.ai embedding models using the `.embedding()` factory method.
For more on embedding models with the AI SDK see [embed()](/docs/reference/ai-sdk-core/embed).

```ts
import { togetherai } from '@ai-sdk/togetherai';
import { embed } from 'ai';

const { embedding } = await embed({
  model: togetherai.embedding('togethercomputer/m2-bert-80M-2k-retrieval'),
  value: 'sunny day at the beach',
});
```

### Model Capabilities

| Model                                            | Dimensions | Max Tokens |
| ------------------------------------------------ | ---------- | ---------- |
| `togethercomputer/m2-bert-80M-2k-retrieval`      | 768        | 2048       |
| `togethercomputer/m2-bert-80M-8k-retrieval`      | 768        | 8192       |
| `togethercomputer/m2-bert-80M-32k-retrieval`     | 768        | 32768      |
| `WhereIsAI/UAE-Large-V1`                         | 1024       | 512        |
| `BAAI/bge-large-en-v1.5`                         | 1024       | 512        |
| `BAAI/bge-base-en-v1.5`                          | 768        | 512        |
| `sentence-transformers/msmarco-bert-base-dot-v5` | 768        | 512        |
| `bert-base-uncased`                              | 768        | 512        |

<Note>
  For a complete list of available embedding models, see the [Together.ai models
  page](https://docs.together.ai/docs/serverless-models#embedding-models).
</Note>

## Reranking Models

You can create Together.ai reranking models using the `.reranking()` factory method.
For more on reranking with the AI SDK see [rerank()](/docs/reference/ai-sdk-core/rerank).

```ts
import { togetherai } from '@ai-sdk/togetherai';
import { rerank } from 'ai';

const documents = [
  'sunny day at the beach',
  'rainy afternoon in the city',
  'snowy night in the mountains',
];

const { ranking } = await rerank({
  model: togetherai.reranking('Salesforce/Llama-Rank-v1'),
  documents,
  query: 'talk about rain',
  topN: 2,
});

console.log(ranking);
// [
//   { originalIndex: 1, score: 0.9, document: 'rainy afternoon in the city' },
//   { originalIndex: 0, score: 0.3, document: 'sunny day at the beach' }
// ]
```

Together.ai reranking models support additional provider options for object documents. You can specify which fields to use for ranking:

```ts
import { togetherai } from '@ai-sdk/togetherai';
import { rerank } from 'ai';

const documents = [
  {
    from: 'Paul Doe',
    subject: 'Follow-up',
    text: 'We are happy to give you a discount of 20%.',
  },
  {
    from: 'John McGill',
    subject: 'Missing Info',
    text: 'Here is the pricing from Oracle: $5000/month',
  },
];

const { ranking } = await rerank({
  model: togetherai.reranking('Salesforce/Llama-Rank-v1'),
  documents,
  query: 'Which pricing did we get from Oracle?',
  providerOptions: {
    togetherai: {
      rankFields: ['from', 'subject', 'text'], // Specify which fields to rank by
    },
  },
});
```

The following provider options are available:

- **rankFields** _string[]_

  Array of field names to use for ranking when documents are JSON objects. If not specified, all fields are used.

### Model Capabilities

| Model                                 |
| ------------------------------------- |
| `Salesforce/Llama-Rank-v1`            |
| `mixedbread-ai/Mxbai-Rerank-Large-V2` |
